mlsanityData to 7 Oct 2026

Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection

arXiv 2408.00286 ↗First posted 1 Aug 2024cs.CVcited by 7

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

3D object detection is essential for understanding 3D scenes. Contemporary techniques often require extensive annotated training data, yet obtaining point-wise annotations for point clouds is time-consuming and laborious. Recent developments in semi-supervised methods seek to mitigate this problem by employing a teacher-student framework to generate pseudo-labels for unlabeled point clouds. However, these pseudo-labels frequently suffer from insufficient diversity and inferior quality. To overcome these hurdles, we introduce an Agent-based Diffusion Model for Semi-supervised 3D Object Detection (Diff3DETR). Specifically, an agent-based object query generator is designed to produce object queries that effectively adapt to dynamic scenes while striking a balance between sampling locations and content embedding. Additionally, a box-aware denoising module utilizes the DDIM denoising process and the long-range attention in the transformer decoder to refine bounding boxes incrementally. Extensive experiments on ScanNet and SUN RGB-D datasets demonstrate that Diff3DETR outperforms state-of-the-art semi-supervised 3D object detection methods.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 33 of its 43 references that are in the map. First posted on arXiv 1 Aug 2024.

Builds on

33 of 43 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection5 Dec
  2. 2022DiffusionDet: Diffusion Model for Object Detection17 Nov

Other references · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2020End-to-End Object Detection with Transformerscited by 7,384
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2022Scalable Diffusion Models with Transformerscited by 5,931
  8. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  9. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  10. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  11. 2021Variational Diffusion Modelscited by 1,145
  12. 2018Panoptic Segmentationcited by 796
19 more
  1. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  2. 2022Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioningcited by 362
  3. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  4. 2021A Survey on Deep Semi-supervised Learningcited by 250
  5. 2022A Survey on Generative Diffusion Modelcited by 238
  6. 2021Group-Free 3D Object Detection via Transformerscited by 215
  7. 2020H3DNet: 3D Object Detection Using Hybrid Geometric Primitivescited by 123
  8. 2022A Generalist Framework for Panoptic Segmentation of Images and Videoscited by 99
  9. 2019SESS: Self-Ensembling Semi-Supervised 3D Object Detectioncited by 89
  10. 20203DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detectioncited by 89
  11. 2020MLCVNet: Multi-Level Context VoteNet for 3D Object Detectioncited by 80
  12. 2020Generative Sparse Detection Networks for 3D Single-shot Object Detectioncited by 69
  13. 2022CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Cloudscited by 58
  14. 2023Diffusion Models Beat GANs on Image Classificationcited by 57
  15. 2023V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detectioncited by 34
  16. 2023Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detectioncited by 24
  17. 2024Semi-supervised 3D Object Detection with PatchTeacher and PillarMixcited by 9
  18. 2023Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detectioncited by 6
  19. 2020Finding Your (3D) Center: 3D Object Detection Using a Learned Losscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 1.

7 papers.

  1. 2024DN-4DGS: Denoised Deformable Network with Temporal-Spatial Aggregation for Dynamic Scene Renderingcited by 28
  2. 2025Relation3D: Enhancing Relation Modeling for Point Cloud Instance Segmentationcited by 10
  3. 2025SAS: Segment Any 3D Scene with Integrated 2D Priorscited by 5
  4. 2025CoDiff: Conditional Diffusion Model for Collaborative 3D Object Detectioncited by 4
  5. 2025Beyond the Final Layer: Hierarchical Query Fusion Transformer with Agent-Interpolation Initialization for 3D Instance Segmentationcited by 3
  6. 2025PGOV3D: Open-Vocabulary 3D Semantic Segmentation with Partial-to-Global Curriculumcited by 3
  7. 2026GeoGuide: Hierarchical Geometric Guidance for Open-Vocabulary 3D Semantic Segmentationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2023DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection
  2. 2023Diffusion-based 3D Object Detection with Random Boxes
  3. 2024DiffuBox: Refining 3D Object Detection with Point Diffusion
  4. 2025Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion
  5. 20233DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features
  6. 2022Semi-supervised 3D Object Detection via Temporal Graph Neural Networks
  7. 2023DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection
  8. 2023Diffusing More Objects for Semi-Supervised Domain Adaptation with Less Labeling